Using a causal decomposition approach to estimate the contribution of employment to differences in mental health profiles between men and women
Notice bibliographique
Résumé
Background: Mental health disorders are known to manifest differently in men and women, however our understanding of how gender interacts with mental health and well-being as a broader construct remains limited. Employment is a key determinant of mental health and there are historical differences in occupational roles among men and women that continue to influence working lives (Bonde, 2008; Cabezas-Rodríguez, Utzet, & Bacigalupe, 2021; Drolet, 2022; Gedikli, Miraglia, Connolly, Bryan, & Watson, 2023; Moyser, 2017; Niedhammer, Bertrais, & Witt, 2021; Stier & Yaish, 2014; Van der Doef & Maes, 1999). This study aims to explore differences in multidimensional mental health between men and women, and to quantify how these differences may change if women had the same employment characteristics as men. Methods: Working-age adults (25-64) were identified through a household survey in Ontario, Canada during 2012. We created multifaceted measures of employment to capture both employment and job quality, as well as multidimensional mental health profiles that capture mental health disorders and well-being using survey data. A causal decomposition approach with Monte Carlo simulation methods estimated the change in differences in mental health profiles between men and women, if women had the same employment characteristics as men. Results: Among 2458 eligible respondents, women were more likely to exhibit clinical mood disorders compared to men, with men more likely to exhibit absence of flourishing without a diagnosable disorder. Among those who were flourishing, women more often expressed at least some life stress compared to men. When women were assigned men's employment characteristics, which amounted to an increase in employment and higher quality employment, some of the gender differences in risk of clinical mood disorder decreased. However, differences between men and women in the remaining mental health profiles increased. Conclusions: This study provided an estimate of the contribution of employment to the observed differences in multidimensional mental health between men and women. This adds to the literature by including a broader range of mental health indicators than disorders alone, and by formalizing the causal framework used to study these relationships.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».